# Copyright (C) 2023, Advanced Micro Devices, Inc. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause

# Part of this code has been re-adapted from https://github.com/yhhhli/BRECQ
# under the following LICENSE:

# MIT License

# Copyright (c) 2021 Yuhang Li

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

import functools
import re
from typing import Any
from typing import Callable
from typing import Dict
from typing import Optional
from typing import Tuple
from typing import Union
import warnings

from accelerate.utils.operations import send_to_device
import torch
from torch import nn
from torch.utils.data.dataloader import DataLoader

from brevitas import config
from brevitas.nn.quant_layer import QuantWeightBiasInputOutputLayer as QuantWBIOL
from brevitas.quant_tensor import QuantTensor
from brevitas_examples.common.learned_round.learned_round_optimizer import Cache
from brevitas_examples.common.learned_round.learned_round_optimizer import get_blocks
from brevitas_examples.common.learned_round.learned_round_optimizer import LearnedRoundOptimizer
from brevitas_examples.common.learned_round.learned_round_parser import parse_learned_round
from brevitas_examples.common.learned_round.learned_round_parser import \
    parse_learned_round_loss_class
from brevitas_examples.common.learned_round.learned_round_parser import parse_lr_scheduler_class
from brevitas_examples.common.learned_round.learned_round_parser import parse_optimizer_class

config.IGNORE_MISSING_KEYS = True


def is_block(module: nn.Module, module_name: str, reg_exp: str = r"layer\d+") -> bool:
    return (re.search(reg_exp, module_name) is not None)


def is_layer(module: nn.Module, module_name: str) -> bool:
    return isinstance(module, QuantWBIOL)


BLOCK_CHECK_MAP = {
    "layerwise": is_layer,
    "blockwise": is_block,}


class CacheVision(Cache, dict):

    def __init__(self) -> None:
        super().__init__()
        self.batch_dim = 0
        self.initialize_cache()

    def store_inputs(self, args, kwargs) -> None:
        input_batch = args[0]
        if isinstance(input_batch, QuantTensor):
            input_batch = input_batch.value

        if hasattr(input_batch, 'names') and 'N' in input_batch.names:
            self.batch_dim = input_batch.names.index('N')
            input_batch.rename_(None)
            input_batch = input_batch.transpose(0, self.batch_dim)

        self["inputs"].append(input_batch)

    def store_output(self, output) -> None:
        if self.batch_dim is not None:
            output.rename_(None)
            output = output.transpose(0, self.batch_dim)

        self["output"].append(output)

    def initialize_cache(self) -> None:
        self["inputs"] = []
        self["output"] = []

    def clear_cache(self) -> None:
        del self["inputs"]
        del self["output"]
        self["inputs"] = []
        self["output"] = []

    def sample_batch(self, indices: torch.Tensor) -> Union[Any, torch.Tensor]:
        if isinstance(self["inputs"], list):
            self["inputs"] = torch.cat(self["inputs"], dim=self.batch_dim)
        if isinstance(self["output"], list):
            self["output"] = torch.cat(self["output"], dim=self.batch_dim)

        return self["inputs"][indices], self["output"][indices]

    def __len__(self):
        return (
            len(self["inputs"])
            if isinstance(self["inputs"], list) else self["inputs"].shape[self.batch_dim])


def vision_forward(model: nn.Module, inputs: Any) -> None:
    device = next(model.parameters()).device
    img, _ = inputs
    img = send_to_device(img, device)
    model(img)


def vision_block_forward(block: nn.Module, inputs: Any) -> torch.Tensor:
    device = next(block.parameters()).device
    inputs = send_to_device(inputs, device)
    return block(inputs)


def apply_learned_round(
    model: nn.Module,
    calibration_loader: DataLoader,
    iters: int = 1000,
    learned_round: str = "hard_sigmoid_round",
    learned_round_loss: str = "regularised_mse",
    block_name_attribute: str = r"layer\d+",
    optimizer: str = "adam",
    lr_scheduler: Optional[str] = None,
    optimizer_lr: float = 1e-3,
    batch_size: int = 1,
    use_best_model: bool = False,
    amp_dtype: torch.dtype = torch.float16,
    loss_scaling_factor: float = 1.,
    learned_round_loss_kwargs: Optional[Dict] = None,
    optimizer_kwargs: Optional[Dict] = None,
    lr_scheduler_kwargs: Optional[Dict] = None,
    learned_round_mode: str = "layerwise",
) -> None:
    # Parse strings to obtain the arguments for the optimizer
    learned_round = parse_learned_round(learned_round)
    learned_round_loss_class = parse_learned_round_loss_class(learned_round_loss)
    optimizer_class = parse_optimizer_class(optimizer)
    lr_scheduler_class = parse_lr_scheduler_class(lr_scheduler)

    # Parse method to retrieve de model blocks
    if learned_round_mode == "layerwise":
        block_check_fn = is_layer
    elif learned_round_mode == "blockwise":
        block_check_fn = functools.partial(is_block, reg_exp=block_name_attribute)
    else:
        block_check_fn = is_layer
        warnings.warn(
            f"{learned_round_mode} is not a valid learned round mode. Defaulting to layerwise.")
    get_blocks_fn = functools.partial(get_blocks, block_check_fn=block_check_fn)

    lr_scheduler_kwargs = {
        "start_factor": 1.0,
        "end_factor": 0.0,
        "verbose": False,} if lr_scheduler_kwargs is None else lr_scheduler_kwargs
    learned_round_optimizer = LearnedRoundOptimizer(
        learned_round=learned_round,
        learned_round_loss_class=learned_round_loss_class,
        optimizer_class=optimizer_class,
        lr_scheduler_class=lr_scheduler_class,
        batch_size=batch_size,
        iters=iters,
        use_best_model=use_best_model,
        amp_dtype=amp_dtype,
        loss_scaling_factor=loss_scaling_factor,
        learned_round_loss_kwargs=learned_round_loss_kwargs,
        optimizer_kwargs=optimizer_kwargs,
        lr_scheduler_kwargs=lr_scheduler_kwargs)
    cache = CacheVision()
    learned_round_optimizer.apply_learned_round(
        model=model,
        model_forward=vision_forward,
        block_forward=vision_block_forward,
        data_loader=calibration_loader,
        cache=cache,
        get_blocks_fn=get_blocks_fn,
        keep_gpu=True,
    )
